Andrew Dobson, Dept. of Computer Science

This work provides compact representations for
single- and multi-robot motion planning in the context of prehensile
robot manipulation. This work explores asymptotic near-optimality
and probabilistic near-optimality of these planners. This allows for
lightweight storage of planning structures which are quick to query,
and provides probabilistic bounds on path quality after finite
computation. A compact representations for n-arm manipulation is
given and efficient planning methods for multi-robot planning
involving object hand-offs are provided. This work provides
significant groundwork for asymptotically-optimal integrated task and
motion planning problems using lightweight data structures.